arXiv subjects

Lisa Hampson

Publications and source records attributed to Lisa Hampson.

3 recordsLinked to original sources

arXiv2025

Adaptive clinical trial design with delayed treatment effects using elicited prior distributions

Clinical trials with time-to-event endpoints, such as overall survival (OS) or progression-free survival (PFS), are fundamental for evaluating new treatments, particularly in immuno-oncology. However, modern therapies, such as immunotherapies and targeted treatments, often exhibit delayed effects that challenge traditional trial designs. These delayed effects violate the proportional hazards assumption, which underpins standard statistical methods like the Cox proportional hazards model and the log-rank test. Careful planning is essential to ensure trials are appropriately designed to account for the timing and magnitude of these effects. Without this planning, interim analyses may lead to premature trial termination if the treatment effect is underestimated early in the study. We present an adaptive trial design framework that incorporates prior distributions, elicited from experts, for delayed treatment effects. By addressing the uncertainty surrounding delayed treatment effects, our approach enhances trial efficiency and robustness, minimizing the risk of premature termination and improving the detection of treatment benefits over time. We present an example illustrating how interim analyses, informed by prior distributions, can guide early stopping decisions. To facilitate the implementation of our framework, we have developed free, open-source software that enables researchers to integrate prior distributions into trial planning and decision-making. This software provides a flexible, accessible tool for designing trials that more accurately evaluate modern therapies through adaptive trial designs.

stat.ME↗

arXiv2023

Assurance Methods for designing a clinical trial with a delayed treatment effect

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur.

stat.AP↗

arXiv2020

Efficient adaptive designs for clinical trials of interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this paper we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

q-bio.QM↗